scala>spark.version
res6: String = 2.1.1
scala>val rdd =
sc.parallelize(Seq("""<books><Comments><Comment><Title>Title1.1</Title><Description>Description_1.1</Description></Comment>
<Comment><Title>Title1.2</Title><Description>Description_1.2</Description></Comment>
<Comment><Title>Title1.3</Title><Description>Description_1.3</Description></Comment>
</Comments></books>"""))
rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at parallelize
at <console>:24
scala>import com.databricks.spark.xml.XmlReader
scala>val df = new XmlReader().xmlRdd(spark.sqlContext, rdd)
df: org.apache.spark.sql.DataFrame = [Comments: struct<Comment:
array<struct<Description:string,Title:string>>>]
scala>df.printSchema
root
|-- Comments: struct (nullable = true)
| |-- Comment: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- Description: string (nullable = true)
| | | |-- Title: string (nullable = true)
scala>df.show(false)
+--------------------------------------------------------------------------------------------------+
|Comments
|
+--------------------------------------------------------------------------------------------------+
|[WrappedArray([Description_1.1,Title1.1], [Description_1.2,Title1.2],
[Description_1.3,Title1.3])]|
+--------------------------------------------------------------------------------------------------+
scala>df.withColumn("comment",
explode(df("Comments.Comment"))).select($"comment.Description",
$"comment.Title").show
+---------------+--------+
| Description| Title|
+---------------+--------+
|Description_1.1|Title1.1|
|Description_1.2|Title1.2|
|Description_1.3|Title1.3|
+---------------+--------+
________________________________
From: Talap, Amol <amol.ta...@capgemini.com>
Sent: Thursday, June 29, 2017 9:38 AM
To: Judit Planas; user@spark.apache.org
Subject: RE: SparkSQL to read XML Blob data to create multiple rows
Thanks Judit, Ayan
Judit,
You almost got it. The explode might help here.
But when I tried I see load() doesn’t like to read from xmlcomment column on
oracle_data.
scala> val xmlDF = sqlContext.sql("SELECT * FROM oracle_data")
17/06/29 18:31:58 INFO parse.ParseDriver: Parsing command: SELECT * FROM
oracle_data
17/06/29 18:31:58 INFO parse.ParseDriver: Parse Completed
…
scala> val xmlDF_flattened =
xmlDF.withColumn("xmlcomment",explode(sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load($"xmlcomment")))
<console>:22: error: overloaded method value load with alternatives:
()org.apache.spark.sql.DataFrame <and>
(path: String)org.apache.spark.sql.DataFrame
cannot be applied to (org.apache.spark.sql.ColumnName)
val xmlDF_flattened =
xmlDF.withColumn("xmlcomment",explode(sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load($"xmlcomment")))
Ayan,
Output of books_inexp.show was as below
title, author
Midnight Rain, Ralls, Kim
Maeve Ascendant, Corets, Eva
Regards,
Amol
From: Judit Planas [mailto:judit.pla...@epfl.ch]
Sent: Thursday, June 29, 2017 3:46 AM
To: user@spark.apache.org
Subject: Re: SparkSQL to read XML Blob data to create multiple rows
Hi Amol,
Not sure I understand completely your question, but the SQL function "explode"
may help you:
http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.explode
pyspark.sql module — PySpark 2.1.1
documentation<http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.explode>
spark.apache.org
pyspark.sql.SparkSession Main entry point for DataFrame and SQL functionality.
pyspark.sql.DataFrame A distributed collection of data grouped into named
columns.
Here you can find a nice example:
https://stackoverflow.com/questions/38210507/explode-in-pyspark
[https://cdn.sstatic.net/Sites/stackoverflow/img/apple-touch-i...@2.png?v=73d79a89bded]<https://stackoverflow.com/questions/38210507/explode-in-pyspark>
python - Explode in PySpark - Stack
Overflow<https://stackoverflow.com/questions/38210507/explode-in-pyspark>
stackoverflow.com
I would like to transform from a DataFrame that contains lists of words into a
DataFrame with each word in its own row. How do I do explode on a column in a
DataFrame?
HTH,
Judit
On 29/06/17 09:05, ayan guha wrote:
Hi
Not sure if I follow your issue. Can you please post output of
books_inexp.show()?
On Thu, Jun 29, 2017 at 2:30 PM, Talap, Amol
<amol.ta...@capgemini.com<mailto:amol.ta...@capgemini.com>> wrote:
Hi:
We are trying to parse XML data to get below output from given input sample.
Can someone suggest a way to pass one DFrames output into load() function or
any other alternative to get this output.
Input Data from Oracle Table XMLBlob:
SequenceID
Name
City
XMLComment
1
Amol
Kolhapur
<books><Comments><Comment><Title>Title1.1</Title><Description>Description_1.1</Description><Comment><Title>Title1.2</Title><Description>Description_1.2</Description><Comment><Title>Title1.3</Title><Description>Description_1.3</Description></Comment></Comments></books>
2
Suresh
Mumbai
<books><Comments><Comment><Title>Title2</Title><Description>Description_2</Description></Comment></Comments></books>
3
Vishal
Delhi
<books><Comments><Comment><Title>Title3</Title><Description>Description_3</Description></Comment></Comments></books>
4
Swastik
Bangalore
<books><Comments><Comment><Title>Title4</Title><Description>Description_4</Description></Comment></Comments></books>
Output Data Expected using Spark SQL:
SequenceID
Name
City
Title
Description
1
Amol
Kolhapur
Title1.1
Description_1.1
1
Amol
Kolhapur
Title1.1
Description_1.2
1
Amol
Kolhapur
Title1.3
Description_1.3
2
Suresh
Mumbai
Title2
Description_2
3
Vishal
Delhi
Title3.1
Description_3.1
4
Swastik
Bangalore
Title4
Description_4
I am able to parse single XML using below approach in spark-shell using example
below but how do we apply the same recursively for all rows ?
https://community.hortonworks.com/questions/71538/parsing-xml-in-spark-rdd.html.
val dfX =
sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load("books.xml")
val xData = dfX.registerTempTable("books")
dfX.printSchema()
val books_inexp =sqlContext.sql("select title,author from books where price<10")
books_inexp.show
Regards,
Amol
This message contains information that may be privileged or confidential and is
the property of the Capgemini Group. It is intended only for the person to whom
it is addressed. If you are not the intended recipient, you are not authorized
to read, print, retain, copy, disseminate, distribute, or use this message or
any part thereof. If you receive this message in error, please notify the
sender immediately and delete all copies of this message.
--
Best Regards,
Ayan Guha
This message contains information that may be privileged or confidential and is
the property of the Capgemini Group. It is intended only for the person to whom
it is addressed. If you are not the intended recipient, you are not authorized
to read, print, retain, copy, disseminate, distribute, or use this message or
any part thereof. If you receive this message in error, please notify the
sender immediately and delete all copies of this message.
